scholarly journals Data‐Driven Learning of Reduced‐Order Dynamics for a Parametrized Shallow Water Equation

PAMM ◽  
2021 ◽  
Vol 20 (S1) ◽  
Author(s):  
Süleyman Yıldız ◽  
Pawan Goyal ◽  
Peter Benner ◽  
Bülent Karasözen
Keyword(s):  
Shallow Water ◽  
Shallow Water Equation ◽  
Data Driven ◽  
Reduced Order

Data-driven reduced order modeling based on tensor decompositions and its application to air-wall heat transfer in buildings

SeMA Journal ◽  
2021 ◽  
Author(s):  
M. Azaïez ◽  
T. Chacón Rebollo ◽  
M. Gómez Mármol ◽  
E. Perracchione ◽  
A. Rincón Casado ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Wall Heat Transfer ◽  
Reduced Order ◽  
Tensor Decompositions

scholarly journals Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 266
Author(s):  
Péter German ◽  
Mauricio E. Tano ◽  
Carlo Fiorina ◽  
Jean C. Ragusa
Keyword(s):  
Heat Transfer ◽  
Porous Medium ◽  
Porous Media ◽  
Steady State ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Data Driven ◽  
Reduced Order ◽  
Medium Formulation ◽  
Flow Resistances

This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).

Stream Computation of Shallow Water Equation Solver for FPGA-based 1D Tsunami Simulation

2016 ◽  
Vol 43 (4) ◽  
pp. 82-87 ◽  
Author(s):  
Kentaro Sano ◽  
Fumiya Kono ◽  
Naohito Nakasato ◽  
Alexander Vazhenin ◽  
Stanislav Sedukhin
Keyword(s):  
Shallow Water ◽  
Shallow Water Equation ◽  
Tsunami Simulation
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